Education

• 
  Shanghai Jiao Tong University (SJTU)

  School of Electronic Information and Electrical Engineering
  B.E. in Automation

  Sep. 2015 - Jul. 2019
• 
  University of Chinese Academy of Sciences (UCAS)

  Institute of Automation
  M.S. in Automation

  Sep. 2019 - Jul. 2022

Research Experience

• 
  Autonomous System Group, Intel Labs China (ILC)

  Research Intern

  Aug. 2020 - Dec. 2021
• 
  Institute of Automation, Chinese Academy of Sciences (CASIA)

  Student Researcher

  Sep. 2019 - Jul. 2022
• 
  Institute for AI Industry Research, Tsinghua University (AIR, THU)

  Researcher

  Jul. 2022 - Present

Academic Service

• Program Committee & Area Chair

  Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative-AI Era @ CVPR'25
  Workshop on Cooperative Intelligence for Embodied AI @ ECCV'24

• Journal Reviewer

  IEEE (TIP, TCSVT, TVT, TIV, ITSM), IET (CV, CSR), PR, Neurocomputing

• Conference Reviewer

  CVPR, ICCV, ECCV, ICRA, IROS, ITSC

• Invited Speaker

  Traffic scenes understanding and simulation testing @ ITSC'22

Honors & Awards

• National Scholarship

  2021
• Pan Deng First-class Scholarship, CAS

  2022
• Excellent Scholarship, SJTU

  2018
• China Industrial Intelligence Challenge, State-level Outstanding Award, CAA

  2018

Autonomous Driving (AD)

• Onboard System (Intelligent Vehicle)

  My exploration on vehicle-side environment perception starts from drivable area detection (ITSC’20), and a series perception algorithms are proposed, including a RGB 2D object detection approach (FII-CenterNet, T-VT’21), a semi-supervised learning approach for RGB 2D segmentation (CPCL, T-IP’22), a RGB-T segmentation approach for challenging lighting conditions (SpiderMesh, TechReport’23), and a 3D segmentation approach for large-scale points cloud (SCF-Net, CVPR’21).

• Roadside System (Intelligent Infrastructure)

  Compared with the well-studied vehicle-side perception, roadside perception has several specific challenges, and the exploration is hindered due to the lack of data. A calibration-free BEV representation network is proposed to address calibration noises caused by inevitable natural factors (CBR, IROS’23). A semantic-geometry decoupled contrastive learning framework is introduced to improve roadside perception performance by leveraging vehicle-side data (IROAM, ICRA’25), and the first real-world large-scale dataset for roadside cooperative perception is released with benchmarks to bloom the research on practical I2I perception (RCooper, CVPR’24).

• Cooperative Autonomous Driving System (V2X)

  Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. A scene-level feature cooperative perception approach is proposed (EMIFF, ICRA’24). To enable interpretable instance-level flexible feature interaction, the concept of query cooperation is proposed, and a cooperative perception framework is introduced, which let query stream flow among agents (QUEST, ICRA’24). Besides, motion forecasting can also benefit from learning cooperative trajectory representation ( NeurIPS'24). In addition to focusing on improving individual modules, a pioneering end-to-end cooperative autonomous driving framework is introduced (UniV2X, AAAI’25).

Biomedical Discovery (BD)

• Biomedical Agent System

  Compared with representation learning in physical world, that for biological modality is more complicated.

• Human-Agent Interaction System

  Recent advances in LLMs have shed light on the development of knowledgeable and versatile AI research assistants in various scientific domains. Multimodal large language models bridge the semantic gap between natural language and other modalities, including molecule, protein, and vision. A multimodal large language model is proposed for assisting biomedical research (BioMedGPT, J-BHI’24), and optical chemical structure understanding task is introduced and explored for molecule-centric scientific discovery (OCSU, TechReport’25).

• Multi-Agent Cooperation System

  Multi-agent cooperation is a potential approach to solve complicated scientific research tasks in an autopilot manner. To facilitate the exploration, an agent platform for biomedicine and life science is presented and open-sourced ( OpenBioMed)